Role of Artificial Intelligence in Early Diagnosis and Risk Stratification of Arterial Aneurysms

Abstract

If left undetected or untreated, arterial aneurysms are dangerous vascular conditions that have a significant risk of morbidity and death. For prompt management and better patient outcomes, early identification and precise rupture risk assessment are essential. In order to facilitate more accurate diagnosis and therapeutic decision-making, artificial intelligence (AI), including machine learning and deep learning approaches, has become a potential tool in vascular medicine in recent years. When AI algorithms are used in imaging modalities like computed tomography, magnetic resonance, and ultrasound, they perform better than manual techniques in automated aneurysm recognition, segmentation, and morphological evaluation. Furthermore, by combining extensive clinical, genetic, and imaging data, predictive models may offer risk evaluations tailored to each patient, assisting in the estimation of aneurysm development, rupture likelihood, and anticipated treatment results. In additiony, AI-based solutions may decrease observer variability, increase screening effectiveness, and facilitate individualized treatment planning. Notwithstanding these encouraging advancements, a number of issues still need to be resolved, such as data availability and quality, model interpretability, external validation, and smooth clinical workflow integration. The purpose of this paper is to provide an overview of existing AI applications in artery aneurysm risk assessment and early detection, addressing both their possible advantages and disadvantages. It also outlines potential future paths that might lead to more accurate, customized treatment of aneurysmal illness, including the use of multimodal data, federated learning, and explainable AI frameworks.

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